Pattern Recognition Using Measure Space Metrics

Patterns are considered as normalized measures and distances between them are defined as distances of the corresponding measures using metrics in measure spaces. This idea can be applied for pattern recognition if smeared patterns have to be compared with given ideal patterns. Different metrics are sensitive to different characteristics of the patterns - this is demonstrated in discussing examples. Particular attention is paid to a problem of Quality Control for an artificial fabric, where the distance to uniformity is defined and evaluated; the results are now used in industry.